code: def main(args):
      device = "cuda" if torch.cuda.is_available() else "cpu"
      waveform, sample_rate, _, _ = LJSPEECH("./", download=True)[0]
  
      mel_kwargs = {
          "sample_rate": sample_rate,
          "n_fft": 2048,
          "f_min": 40.0,
          "n_mels": 80,
          "win_length": 1100,
          "hop_length": 275,
          "mel_scale": "slaney",
          "norm": "slaney",
          "power": 1,
      }
      transforms = torch.nn.Sequential(
          MelSpectrogram(**mel_kwargs),
          NormalizeDB(min_level_db=-100, normalization=True),
      )
      mel_specgram = transforms(waveform)
  
      wavernn_model = wavernn(args.checkpoint_name).eval().to(device)
      wavernn_inference_model = WaveRNNInferenceWrapper(wavernn_model)
  
      if args.jit:
          wavernn_inference_model = torch.jit.script(
              wavernn_inference_model
          )
  
      with torch.no_grad():
          output = wavernn_inference_model(
              mel_specgram.to(device),
              mulaw=(not args.no_mulaw),
              batched=(not args.no_batch_inference),
              timesteps=args.batch_timesteps,
              overlap=args.batch_overlap,
          )
  
      torchaudio.save(
          args.output_wav_path, output, sample_rate=sample_rate
      )
  
file_path: audio/examples/pipeline_wavernn/inference.py
imports: import argparse
  import torch
  import torchaudio
  from processing import NormalizeDB
  from torchaudio.datasets import LJSPEECH
  from torchaudio.models import wavernn
  from torchaudio.models.wavernn import _MODEL_CONFIG_AND_URLS
  from torchaudio.transforms import MelSpectrogram
  from wavernn_inference_wrapper import WaveRNNInferenceWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types